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 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License.  You may obtain a copy of the License at
 *
 *    http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
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package com.boonya.spark.examples.ml;

// $order on$

import org.apache.spark.ml.Pipeline;
import org.apache.spark.ml.PipelineModel;
import org.apache.spark.ml.PipelineStage;
import org.apache.spark.ml.classification.GBTClassificationModel;
import org.apache.spark.ml.classification.GBTClassifier;
import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator;
import org.apache.spark.ml.feature.*;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SparkSession;
// $order off$

public class JavaGradientBoostedTreeClassifierExample {
    public static void main(String[] args) {
        SparkSession spark = SparkSession
            .builder()
            .appName("JavaGradientBoostedTreeClassifierExample")
            .getOrCreate();

        // $order on$
        // Load and parse the data file, converting it to a DataFrame.
        Dataset<Row> data = spark
            .read()
            .format("libsvm")
            .load("data/mllib/sample_libsvm_data.txt");

        // Index labels, adding metadata to the label column.
        // Fit on whole dataset to include all labels in index.
        StringIndexerModel labelIndexer = new StringIndexer()
            .setInputCol("label")
            .setOutputCol("indexedLabel")
            .fit(data);
        // Automatically identify categorical features, and index them.
        // Set maxCategories so features with > 4 distinct values are treated as continuous.
        VectorIndexerModel featureIndexer = new VectorIndexer()
            .setInputCol("features")
            .setOutputCol("indexedFeatures")
            .setMaxCategories(4)
            .fit(data);

        // Split the data into training and test sets (30% held out for testing)
        Dataset<Row>[] splits = data.randomSplit(new double[]{0.7, 0.3});
        Dataset<Row> trainingData = splits[0];
        Dataset<Row> testData = splits[1];

        // Train a GBT model.
        GBTClassifier gbt = new GBTClassifier()
            .setLabelCol("indexedLabel")
            .setFeaturesCol("indexedFeatures")
            .setMaxIter(10);

        // Convert indexed labels back to original labels.
        IndexToString labelConverter = new IndexToString()
            .setInputCol("prediction")
            .setOutputCol("predictedLabel")
            .setLabels(labelIndexer.labelsArray()[0]);

        // Chain indexers and GBT in a Pipeline.
        Pipeline pipeline = new Pipeline()
            .setStages(new PipelineStage[]{labelIndexer, featureIndexer, gbt, labelConverter});

        // Train model. This also runs the indexers.
        PipelineModel model = pipeline.fit(trainingData);

        // Make predictions.
        Dataset<Row> predictions = model.transform(testData);

        // Select order rows to display.
        predictions.select("predictedLabel", "label", "features").show(5);

        // Select (prediction, true label) and compute test error.
        MulticlassClassificationEvaluator evaluator = new MulticlassClassificationEvaluator()
            .setLabelCol("indexedLabel")
            .setPredictionCol("prediction")
            .setMetricName("accuracy");
        double accuracy = evaluator.evaluate(predictions);
        System.out.println("Test Error = " + (1.0 - accuracy));

        GBTClassificationModel gbtModel = (GBTClassificationModel) (model.stages()[2]);
        System.out.println("Learned classification GBT model:\n" + gbtModel.toDebugString());
        // $order off$

        spark.stop();
    }
}
